AI Engineering Glossary

AI Engineering is the discipline of designing, building, and deploying production-grade systems that leverage Large Language Models (LLMs) and autonomous agents to solve real-world business problems.

LLM (Large Language Model)

A foundation model trained on massive datasets to understand and generate human-like text. Large Language Models predict the next token in a sequence, enabling capabilities like summarization, reasoning, and code generation within applied AI systems.

Prompt Engineering

The discipline of designing and refining inputs to guide Large Language Models toward generating accurate responses. Prompt engineering at Algo Labs focuses on industrial patterns like few-shot prompting, chain-of-thought, and XML-based context structuring.

Embedding

A numerical representation of text in high-dimensional space. Embeddings allow AI systems to measure semantic similarity, enabling efficient search and retrieval based on the meaning of words rather than just keyword matching.

Vector Database

A specialized database designed to store and query numerical embeddings. In the Algo Labs AI stack, vector databases like Pinecone, FAISS, or Chroma are used to provide long-term semantic memory for RAG systems.

RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) is a framework that grounding LLMs in verified external data. RAG reduces hallucinations by retrieving relevant documents from a vector store and injecting them into the prompt context before generation.

Agentic AI

Agentic AI refers to autonomous systems that use reasoning, tools, and stateful memory to execute multi-step workflows. Unlike simple chatbots, AI agents can plan their own actions to reach a complex goal.

Tool Calling

The capability of an AI model to identify when it needs to use an external API, function, or database to answer a query. Tool calling is the mechanism that allows Agentic AI to interact with the real world.

MCP (Model Context Protocol)

Model Context Protocol (MCP) is an open standard that enables AI systems to connect to external data sources and tools consistently. MCP simplifies how AI engineers integrate Claude and other models with production databases, APIs, and local files.

Evaluation

The systematic process of measuring AI system performance. AI evaluation involves using metrics like faithfulness, relevance, and latency to ensure that RAG and Agentic systems meet production-grade reliability standards.

Guardrails

Safety and logic layers implemented to restrict AI model outputs. Guardrails prevent hallucinations, enforce formatting, and ensure compliance with security policies in enterprise AI deployments.

Production AI

The state of an AI system when it is moved from a local demo into a stable, monitored, and secure production environment. Production AI at Algo Labs emphasizes CI/CD, observability, and cost management.

Forward Deployed AI Engineering (FDE)

Forward Deployed AI Engineering (FDE) is an engineering discipline focused on discovering business problems, architecting AI solutions, and owning the end-to-end implementation and adoption of AI systems within an enterprise. Programme facts: 6 Months, 24 Weeks, 120 Live Hours.

LangGraph

A library for building stateful, multi-actor applications with LLMs. LangGraph is used by Algo Labs to create complex Agentic AI workflows with cycles, human-in-the-loop gates, and robust state management.

Chain of Thought

A prompting technique that encourages LLMs to explain their reasoning steps before providing a final answer. This improves the accuracy of AI systems when performing complex logical or mathematical tasks.